OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems
Venue: arXiv cs.LG 2025
Year: 2025

As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on centralized cloud servers. However, implementing and executing complex algorithms in embedded devices poses significant challenges due to limited computational power, memory, and energy resources. This paper presents algorithmic and hardware techniques to efficiently implement two LinearUCB Contextual Bandits algorithms on resource-constrained embedded devices. Algorithmic modifications based on the Sherman-Morrison-Woodbury formula streamline model complexity, while vector acceleration is harnessed to speed up matrix operations. We analyze the impact of each optimization individually and then combine them in a two-pronged strategy. The results show notable improvements in execution time and energy consumption, demonstrating the effectiveness of combining algorithmic and hardware optimizations to enhance learning models for edge computing environments with low-power and real-time requirements.
Safe and Efficient Robot Action Planning in the Presence of Unconcerned Humans
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is required to predict human actions over a finite future horizon; such predictions are often inaccurate in real-world scenarios. One possible approach to reduce the uncertainties is to provide the robot with the capability of reasoning about the human's awareness of potential dangers. This paper discusses that by using a binary variable, so-called danger awareness coefficient, it is possible to differentiate between concerned and unconcerned humans, and provides a learning algorithm to determine this coefficient by observing human actions. Moreover, this paper argues how humans rely on predictions of other agents' future actions (including those of robots in human-robot interaction) in their decision-making. It also shows that ignoring this aspect in predicting human's future actions can significantly degrade the efficiency of the interaction, causing agents to deviate from their optimal paths. The proposed robot action planning scheme is verified and validated via extensive simulation and experimental studies on a LoCoBot WidowX-250.
MLPs at the EOC: Spectrum of the NTK
Venue: arXiv cs.LG 2025
Year: 2025

We study the properties of the Neural Tangent Kernel (NTK) $\overset{\scriptscriptstyle\infty}{K} : \mathbb{R}^{m_0} \times \mathbb{R}^{m_0} \to \mathbb{R}^{m_l \times m_l}$ corresponding to infinitely wide $l$-layer Multilayer Perceptrons (MLPs) taking inputs from $\mathbb{R}^{m_0}$ to outputs in $\mathbb{R}^{m_l}$ equipped with activation functions $ϕ(s) = a s + b \vert s \vert$ for some $a,b \in \mathbb{R}$ and initialized at the Edge Of Chaos (EOC). We find that the entries $\overset{\scriptscriptstyle\infty}{K}(x_1,x_2)$ can be approximated by the inverses of the cosine distances of the activations corresponding to $x_1$ and $x_2$ increasingly better as the depth $l$ increases. By quantifying these inverse cosine distances and the spectrum of the matrix containing them, we obtain tight spectral bounds for the NTK matrix $\overset{\scriptscriptstyle\infty}{K} = [\frac{1}{n} \overset{\scriptscriptstyle\infty}{K}(x_{i_1},x_{i_2}) : i_1, i_2 \in [1:n]]$ over a dataset $\{x_1,\cdots,x_n\} \subset \mathbb{R}^{m_0}$, transferred from the inverse cosine distance matrix via our approximation result. Our results show that $Δ_ϕ= \frac{b^2}{a^2+b^2}$ determines the rate at which the condition number of the NTK matrix converges to its limit as depth increases, implying in particular that the absolute value ($Δ_ϕ=1$) is better than the ReLU ($Δ_ϕ=\frac{1}{2}$) in this regard.
Active RIS-Assisted URLLC NOMA-Based 5G Network with FBL under Jamming Attacks
Venue: arXiv cs.CR 2025
Year: 2025

In this paper, we tackle the challenge of jamming attacks in Ultra-Reliable Low Latency Communication (URLLC) within Non-Orthogonal Multiple Access (NOMA)-based 5G networks under Finite Blocklength (FBL) conditions. We introduce an innovative approach that employs Reconfigurable Intelligent Surfaces (RIS) with active elements to enhance energy efficiency while ensuring reliability and meeting latency requirements. Our approach incorporates the traffic model, making it practical for real-world scenarios with dynamic traffic loads. We thoroughly analyze the impact of blocklength and packet arrival rate on network performance metrics and investigate the optimal amplitude value and number of RIS elements. Our results indicate that increasing the number of RIS elements from 4 to 400 can improve signal-to-jamming-plus-noise ratio (SJNR) by 13.64\%. Additionally, optimizing blocklength and packet arrival rate can achieve a 31.68% improvement in energy efficiency and reduced latency. These findings underscore the importance of optimized settings for effective jamming mitigation.
"See You Later, Alligator": Impacts of Robot Small Talk on Task, Rapport, and Interaction Dynamics in Human-Robot Collaboration
Venue: arXiv cs.RO 2025
Year: 2025

Small talk can foster rapport building in human-human teamwork; yet how non-anthropomorphic robots, such as collaborative manipulators commonly used in industry, may capitalize on these social communications remains unclear. This work investigates how robot-initiated small talk influences task performance, rapport, and interaction dynamics in human-robot collaboration. We developed an autonomous robot system that assists a human in an assembly task while initiating and engaging in small talk. A user study ($N = 58$) was conducted in which participants worked with either a functional robot, which engaged in only task-oriented speech, or a social robot, which also initiated small talk. Our study found that participants in the social condition reported significantly higher levels of rapport with the robot. Moreover, all participants in the social condition responded to the robot's small talk attempts; 59% initiated questions to the robot, and 73% engaged in lingering conversations after requesting the final task item. Although active working times were similar across conditions, participants in the social condition recorded longer task durations than those in the functional condition. We discuss the design and implications of robot small talk in shaping human-robot collaboration.
Bypassing Array Canaries via Autonomous Function Call Resolution
Venue: arXiv cs.CR 2025
Year: 2025

We observed the Array Canary, a novel JavaScript anti-analysis technique currently exploited in-the-wild by the Phishing-as-a-Service framework Darcula. The Array Canary appears to be an advanced form of the array shuffling techniques employed by the Emotet JavaScript downloader. In practice, a series of Array Canaries are set within a string array and if modified will cause the program to endlessly loop. In this paper, we demonstrate how an Array Canary works and discuss Autonomous Function Call Resolution (AFCR), which is a method we created to bypass Array Canaries. We also introduce Arphsy, a proof-of-concept for AFCR designed to guide Large Language Models and security researchers in the deobfuscation of "canaried" JavaScript code. We accomplish this by (i) Finding and extracting all Immediately Invoked Function Expressions from a canaried file, (ii) parsing the file's Abstract Syntax Tree for any function that does not implement imported function calls, (iii) identifying the most reassigned variable and its corresponding function body, (iv) calculating the length of the largest string array and uses it to determine the offset values within the canaried file, (v) aggregating all the previously identified functions into a single file, and (vi) appending driver code into the verified file and using it to deobfuscate the canaried file.
From Following to Understanding: Investigating the Role of Reflective Prompts in AR-Guided Tasks to Promote Task Understanding
Venue: arXiv cs.HC 2025
Year: 2025

Augmented Reality (AR) is a promising medium for guiding users through tasks, yet its impact on fostering deeper task understanding remains underexplored. This paper investigates the impact of reflective prompts -- strategic questions that encourage users to challenge assumptions, connect actions to outcomes, and consider hypothetical scenarios -- on task comprehension and performance. We conducted a two-phase study: a formative survey and co-design sessions (N=9) to develop reflective prompts, followed by a within-subject evaluation (N=16) comparing AR instructions with and without these prompts in coffee-making and circuit assembly tasks. Our results show that reflective prompts significantly improved objective task understanding and resulted in more proactive information acquisition behaviors during task completion. These findings highlight the potential of incorporating reflective elements into AR instructions to foster deeper engagement and learning. Based on data from both studies, we synthesized design guidelines for integrating reflective elements into AR systems to enhance user understanding without compromising task performance.
Threat-based Security Controls to Protect Industrial Control Systems
Venue: arXiv cs.CR 2025
Year: 2025

This paper analyzes the reported threats to Industrial Control Systems (ICS)/Operational Technology (OT) and identifies common tactics, techniques, and procedures (TTP) used by threat actors. The paper then uses the MITRE ATT&CK framework to map the common TTPs and provide an understanding of the security controls needed to defend against the reported ICS threats. The paper also includes a review of ICS testbeds and ideas for future research using the identified controls.
PCSI -- The Platform for Content-Structure Inference
Venue: arXiv cs.IR 2025
Year: 2025

The Platform for Content-Structure Inference (PCSI, pronounced "pixie") facilitates the sharing of information about the process of converting Web resources into structured content objects that conform to a predefined format. PCSI records encode methods for deriving structured content from classes of URLs, and report the results of applying particular methods to particular URLs. The methods are scripts written in Hex, a variant of Awk with facilities for traversing the HTML DOM.
Extraction of Secrets from 40nm CMOS Gate Dielectric Breakdown Antifuses by FIB Passive Voltage Contrast
Venue: arXiv cs.CR 2025
Year: 2025

CMOS one-time-programmable (OTP) memories based on antifuses are widely used for storing small amounts of data (such as serial numbers, keys, and factory trimming) in integrated circuits due to their low cost, requiring no additional mask steps to fabricate. Device manufacturers and IP vendors have claimed for years that antifuses are a ``high security" memory which is significantly more difficult for an attacker to extract data from than other types of memory, such as Flash or mask ROM - however, as our results show, this is untrue. In this paper, we demonstrate that data bits stored in a widely used antifuse block can be extracted by a semiconductor failure analysis technique known as passive voltage contrast (PVC) using a focused ion beam (FIB). The simple form of the attack demonstrated here recovers the bitwise OR of two physically adjacent memory rows sharing common metal 1 contacts, however we have identified several potential mechanisms by which it may be possible to read the even and odd rows separately. We demonstrate the attack on a commodity microcontroller made on the 40nm node and show how it can be used to extract significant quantities of sensitive data, such as keys for firmware encryption, in time scales which are very practical for real world exploitation (1 day of sample prep plus a few hours of FIB time) with only a single target device required after initial reconnaissance has been completed on blank devices.
Markovian Search with Ex-Ante Constraints: Theory and Applications to Socially Aware Algorithmic Hiring
Venue: arXiv cs.DS 2025
Year: 2025

We develop an algorithmic framework to incorporate "ex-ante" constraints on outcomes (that hold only on average) into stateful sequential search with costly inspection. Our framework encompasses the classical Weitzman's Pandora's box [Weitzman, 1979] as well as its extensions to joint Markovian scheduling [Dumitriu et al., 2003; Gittins, 1979], modeling richer processes such as multistage search with multiple layers of inspection. Ex-ante constraints in search are particularly motivated by social considerations in algorithmic hiring, where they adjust outcome distributions to promote equity and access. Building on the optimality of index-based policies in the unconstrained problems, we show that optimal policies under a single ex-ante constraint (e.g., demographic parity) retain an index-based structure but require (i) dual-based adjustments of the indices and (ii) randomization between two such adjustments via a "tie-breaking rule," both easy to compute and economically interpretable. We then extend our results to handle multiple affine constraints by reduction to a variant of the exact Carathéodory problem and providing a polynomial-time algorithm to construct an optimal randomized dual-adjusted index-based policy that satisfies all constraints simultaneously. For general affine and convex constraints, we develop a primal-dual algorithm that randomizes over a polynomial number of dual-based adjustments, yielding a near-feasible, near-optimal policy. All these results rely on the key observation that a suitable relaxation of the Lagrange dual function for these constrained problems admits index-based policies akin to those in the unconstrained setting. Finally, through a numerical study, we investigate the implications of various socially aware ex-ante constraints on the utilitarian loss (price of fairness), and examine whether they achieve their intended socially desirable outcomes.
Task Allocation in Customer-led Two-sided Markets with Satellite Constellation Services
Venue: arXiv cs.GT 2025
Year: 2025

Multi-agent systems (MAS) are increasingly applied to complex task allocation in two-sided markets, where agents such as companies and customers interact dynamically. Traditional company-led Stackelberg game models, where companies set service prices, and customers respond, struggle to accommodate diverse and personalised customer demands in emerging markets like crowdsourcing. This paper proposes a customer-led Stackelberg game model for cost-efficient task allocation, where customers initiate tasks as leaders, and companies create their strategies as followers to meet these demands. We prove the existence of Nash Equilibrium for the follower game and Stackelberg Equilibrium for the leader game while discussing their uniqueness under specific conditions, ensuring cost-efficient task allocation and improved market performance. Using the satellite constellation services market as a real-world case, experimental results show a 23% reduction in customer payments and a 6.7-fold increase in company revenues, demonstrating the model's effectiveness in emerging markets.
A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks
Venue: arXiv cs.AR 2025
Year: 2025

The softmax function is a widely used activation function in the output layers of neural networks, responsible for converting raw scores into class probabilities while introducing essential non-linearity. Implementing Softmax efficiently poses challenges on low-end FPGAs due to limited hardware resources and the computational complexity of exponential and division operations. This work evaluates approximate computing techniques for softmax acceleration using Taylor series and interpolation methods using Look-Up Tables (LUTs). These approximations aim to reduce execution time and resource consumption while maintaining acceptable levels of numerical precision. Our findings show that quadratic interpolation with LUTs yields the lowest numerical error. In contrast, Taylor-based approximations offer significantly better performance in terms of execution time and resource efficiency due to their computational simplicity. When applied to real-world deep learning models such as LeNet-5 and MobileNet v2, the first- and second-order Taylor approximations provided substantial trade-offs between accuracy and resource savings, achieving up to 0.2% accuracy degradation and 14% resource reduction compared to exact implementations. These results highlight the effectiveness of approximate Softmax designs on resource-constrained FPGAs and lay the groundwork for their integration into larger models, including large language models (LLMs).
Joint Power and Bit Allocation for Precoded Massive MIMO Channels
Venue: arXiv cs.IT 2025
Year: 2025

This work addresses the joint optimization of power and bit allocation in precoded large-scale n x n MIMO systems with discrete input alphabets, specifically QAM constellations. We propose an adaptive QAM scheme that maintains a fixed gap to the Gaussian-input capacity for a given n. A key finding is that, under the proposed scheme, the mercury/waterfilling (MWF) solution reduces analytically to the classical water-filling (WF) policy. Furthermore, the adaptive QAM configuration can be precomputed under the large-system assumption, enabling the replacement of full SVD with truncated SVD and yielding substantial computational savings. To support practical deployment, we develop a bit-allocation algorithm that meets a target transmission data rate while minimizing the overall decoding error rate and preserving computational complexity at O(n log n). Simulation results confirm that the proposed truncated SVD precoding, paired with the joint power and bit allocation, achieves superior decoding performance relative to conventional approaches, while operating at significantly lower complexity.
Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units
Venue: arXiv cs.PF 2025
Year: 2025

This study presents a reconstruction of the Gaussian Beam Tracing solution using CUDA, with a particular focus on the utilisation of GPU acceleration as a means of overcoming the performance limitations of traditional CPU algorithms in complex acoustic simulations. The algorithm is implemented and optimised on the NVIDIA RTX A6000 GPU, resulting in a notable enhancement in the performance of the Gaussian Beam Summation (GBS) process. In particular, the GPU-accelerated GBS algorithm demonstrated a significant enhancement in performance, reaching up to 790 times faster in city enviroment and 188 times faster in open plane enviroment compared to the original CPU-based program. To address the challenges of acceleration, the study introduce innovative solutions for handling irregular loops and GPU memory limitations, ensuring the efficient processing of large quantities of rays beyond the GPU's single-process capacity. Furthermore, this work established performance evaluation strategies crucial for analysing and reconstructing similar algorithms. Additionally, the study explored future directions for further accelerating the algorithm, laying the groundwork for ongoing improvements.
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits
Venue: arXiv cs.LG 2025
Year: 2025

We study lifelong learning in linear bandits, where a learner interacts with a sequence of linear bandit tasks whose parameters lie in an $m$-dimensional subspace of $\mathbb{R}^d$, thereby sharing a low-rank representation. Current literature typically assumes that the tasks are diverse, i.e., their parameters uniformly span the $m$-dimensional subspace. This assumption allows the low-rank representation to be learned before all tasks are revealed, which can be unrealistic in real-world applications. In this work, we present the first nontrivial result for sequential multi-task linear bandits without the task diversity assumption. We develop an algorithm that efficiently learns and transfers low-rank representations. When facing $N$ tasks, each played over $τ$ rounds, our algorithm achieves a regret guarantee of $\tilde{O}\big (Nm \sqrtτ + N^{\frac{2}{3}} τ^{\frac{2}{3}} d m^{\frac13} + Nd^2 + τm d \big)$ under the ellipsoid action set assumption. This result can significantly improve upon the baseline of $\tilde{O} \left (Nd \sqrtτ\right)$ that does not leverage the low-rank structure when the number of tasks $N$ is sufficiently large and $m \ll d$. We also demonstrate empirically on synthetic data that our algorithm outperforms baseline algorithms, which rely on the task diversity assumption.
Can Large Language Models Understand Preferences in Personalized Recommendation?
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) excel in various tasks, including personalized recommendations. Existing evaluation methods often focus on rating prediction, relying on regression errors between actual and predicted ratings. However, user rating bias and item quality, two influential factors behind rating scores, can obscure personal preferences in user-item pair data. To address this, we introduce PerRecBench, disassociating the evaluation from these two factors and assessing recommendation techniques on capturing the personal preferences in a grouped ranking manner. We find that the LLM-based recommendation techniques that are generally good at rating prediction fail to identify users' favored and disfavored items when the user rating bias and item quality are eliminated by grouping users. With PerRecBench and 19 LLMs, we find that while larger models generally outperform smaller ones, they still struggle with personalized recommendation. Our findings reveal the superiority of pairwise and listwise ranking approaches over pointwise ranking, PerRecBench's low correlation with traditional regression metrics, the importance of user profiles, and the role of pretraining data distributions. We further explore three supervised fine-tuning strategies, finding that merging weights from single-format training is promising but improving LLMs' understanding of user preferences remains an open research problem. Code and data are available at https://github.com/TamSiuhin/PerRecBench
Time Series Embedding Methods for Classification Tasks: A Review
Venue: arXiv cs.LG 2025
Year: 2025

Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. Due to their multidimensional nature, time series often need to be embedded into a fixed-dimensional feature space to enable processing with various machine learning algorithms. In this paper, we present a comprehensive review and quantitative evaluation of time series embedding methods for effective representations in machine learning and deep learning models. We introduce a taxonomy of embedding techniques, categorizing them based on their theoretical foundations and application contexts. Our work provides a quantitative evaluation of representative methods from each category by assessing their performance on downstream classification tasks across diverse real-world datasets. Our experimental results demonstrate that the performance of embedding methods varies significantly depending on the dataset and classification algorithm used, highlighting the importance of careful model selection and extensive experimentation for specific applications. To facilitate further research and practical applications, we provide an open-source code repository implementing these embedding methods. This study contributes to the field by offering a systematic comparison of time series embedding techniques, guiding practitioners in selecting appropriate methods for their specific applications, and providing a foundation for future advancements in time series analysis.